Foundations of Quantization for Probability Distributions
Due to the rapidly increasing need for methods of data compression, quantization has become a flourishing field in signal and image processing and information theory. The same techniques are also used in statistics (cluster analysis), pattern recognition, and operations research (optimal location of...
Shranjeno v:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serija: | Lecture notes in mathematics
1730 |
| Teme: | |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Foundations of quantization for probability distributions, Siegfried Graf, Harald Luschgy, 2000, Berlin, Springer, 1 vol. (X-230 p.), Lecture notes in mathematics, 3-540-67394-6 • Foundations of Quantization for Probability Distributions, Texte imprimé, 9783662168271 |
Kazalo:
- I. General properties of the quantization for probability distributions: Voronoi partitions. Centers and moments of probability distributions. The quantization problem. Basic properties of optimal quantizers. Uniqueness and optimality in one dimension
- II. Asymptotic quantization for nonsingular probability distributions: Asymptotics for the quantization error. Asymptotically optimal quantizers. Regular quantizers and quantization coefficients. Random quantizers and quantization coefficients. Asymptotics for the covering radius
- III. Asymptotic quantization for singular probability distributions: The quantization dimension. Regular sets and measures of dimension D. Rectifiable curves. Self-similar sets and measures.